The ability to perform monolingual text-to-text generation is an
important step in solving many natural language processing problems.
For example, when generating novel text at the sentence-level,
abstractive summarization systems may need to compress sentences or
fuse multiple sentences together; the evaluation of translation
systems may require additional paraphrases to use as reference gold
standards; and answers to questions may need to be generated
automatically from extracted sentences.

The community of researchers examining monolingual text-to-text
generation has grown steadily in recent years, introducing the need
for a focused venue to communicate results in this area. As tools and
approaches are developed, it is important that our community shares
its experiences and its resources.

This workshop will solicit work describing the use of data-oriented
text-to-text generation methods, where the generation process begins
with some source text as input. As such, it complements existing
events such as GenChal'11 at ENLG 2011, which will have a focus on
data-to-text surface realisation methods.

This year, the workshop will focus on work describing the generation of
novel sentences, with preference given to submissions that describe
how the proposed text-to-text generation model has an impact on
content selection and/or issues of grammaticality at the sentence level.
Submissions can describe work-in-progress, resources, position papers
as well as traditional unpublished work.

We will be accepting both short (up to four (4) pages of content, and two (2) additional pages of references) and long papers (up to eight (8) pages of content, with two (2) additional pages of references). Submission requirements are identical to that of the main conference. For further information on the submission guidelines see: http://www.acl2011.org/call.shtml#submission .

Please submit you paper using the START V2 system. Note that short papers can be submitted on April 9 (one day after the ACL short paper notification) but the abstracts must be emailed to the organizers before April 1.

Statistical techniques are now the dominant approach for NLP problems like translation and parsing, where data occur as a by-product of human activities (parallel corpora from international organizations) or can be obtained by expert annotation efforts. Statistical models for text-to-text problems must deal with scenarios where data are less "natural," less static, and generally smaller. I'll present and attempt to synthesize examples from my group's efforts in this area, drawing from examples in machine translation, question answering, question generation, paraphrase, and summarization.